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Management accounting plays a greater role in providing business decisions for enterprises. As the core technology of big data processing, data mining technology can process a large amount of complex, structured, and unstructured information very efficiently. In this paper, the nonlinear system is taken as the research object, and the obtained system data is directly used. In the framework of divide and conquer strategy, multi-model method is combined with data clustering and a local modeling algorithm to study the nonlinearity of management accounting data mining. Through online adaptive estimation of parameters, aiming at the zero dynamic problem of the non-minimum phase system, through the decoupling matrix analysis of the nonlinear system, the state of the original strategy is dynamically extended to make the estimated parameters asymptotically approach the true value. The estimated parameters are applied to the nonlinear hypothesis strategy to achieve the goal of linearization control. It is assumed that the generation strategy is applied to the mining of different related types of rules without manually setting parameters, which greatly improves the efficiency of rule discovery. Through case analysis, this paper demonstrates the feasibility of applying nonlinear hypothesis strategy of data mining technology to management accounting and puts forward the relevant application process framework. The validity and feasibility of nonlinear hypothesis strategy data mining technology in management accounting are verified. [ABSTRACT FROM AUTHOR]

In the context of the continuous promotion of China's big data development strategy, this paper quantitively analyses China's existing national-level big data policies from the perspective of policy instruments and coword networks, discusses the rationality of existing policies, explores ways to improve policies, and provides a reference for the innovation of China's big data policies. This paper carries out a quantitative textual analysis of China's national big data policy from the perspective of policy instruments using word frequency analysis to obtain a keyword coword matrix and visualization analysis tools to obtain a coword network. This paper further studies the network node characteristics and structure using social network analysis methods, including degree centrality, clustering analysis, and multidimensional scale analysis, to identify the policy structure and characteristics. Improving big data policy requires improvements in policy instruments on the supply side, resolving existing policy gaps, and strengthening coordination with other policies. [ABSTRACT FROM AUTHOR]

In order to solve the problems of inconsistent accounting and untimely accounting information, this paper proposes a research method of intelligent optimization of financial sharing path based on big data of accounting. This paper analyzes the current working situation of the financial sharing service center of large- and medium-sized enterprises and expounds the reasons for the blockchain. The author embeds the blockchain technology in the financial sharing service center, with a view to optimizing the design of the current FSSC's functions in terms of the scope of daily business work, the effectiveness of accounting information processing, and the utilization of financial analysis decisions, and finally analyzes the corporate effect of applying the financial sharing architecture based on blockchain technology. The results show that compared with the financial sharing score under the traditional mode, the financial sharing score under the blockchain technology is higher, and the former is 69.675 points, and the latter is 80.6340 points. From this, we can conclude that financial sharing under blockchain technology has significant advantages. This framework realizes the effective allocation of enterprise information resources through finance to achieve real industry finance integration. [ABSTRACT FROM AUTHOR]

In this paper, the clustering algorithm of big data fuzzy K-means is used to research and analyze the teaching management system. Since the values of the indicator variables of bad units are usually significantly different in smooth areas and discontinuous areas, we cluster the indicators of local areas through K-means clustering, so that only good units or bad units are included in each class. The server is deployed on the campus and is networked with the teaching computers of each classroom. Teachers log in to the class rate management system before or during class, log in to their respective users, select the corresponding class, and make corresponding attendance records according to the attendance of students in the class. The system automatically counts the number of expected arrivals, attendance, late arrivals, leave requests, and absenteeism for the class. Teachers can also fill in information feedback for this class: a list of absenteeism, classroom discipline, student learning status, teaching suggestions, and teaching testimonials. The development environment built by this system is a combination of PHP + MYSQL, which reasonably planned according to the overall needs. The development mode adopts the top-down model, which runs through the whole process from system development to testing to application. The most used floating point number encoding is the decimal floating point number encoding. The relationship of each module of the virtual reality teaching management system is sorted out, the overall framework of the virtual reality teaching management system is established, and the design of the business flow diagram of the management system is completed. This paper also uses Oracle database technology to further explain the design process of designing the system database. Finally, combined with the system design and implementation, the system test is completed from two aspects: system function test and performance test. [ABSTRACT FROM AUTHOR]

With the increasing number of college students, the difficulty and workload of ideological and political education are also increasing. For the calculation and analysis of big data, it is most suitable to use data mining algorithm for calculation and research. In order to help college students' ideological and political education to develop and innovate, this paper establishes a new model of college students' ideological and political education combined with particle swarm optimization algorithm to help carry out college students' ideological and political education. The particle swarm optimization algorithm is tested. Therefore, this paper uses data mining algorithm to calculate and analyze the research in this paper. The algorithm is tested and analyzed. Through the test of data mining algorithm, the calculation accuracy of the algorithm is proved, and the feasibility of this paper is proved. This paper studies the ideological and political education by using data mining algorithm. The computational efficiency of the optimized data mining algorithm is twice that of the traditional algorithm, and the optimized algorithm has also been significantly improved. In general, the data mining algorithm optimized in this paper is reasonable, which can improve the calculation accuracy and efficiency of the algorithm. [ABSTRACT FROM AUTHOR]

In music teaching in high schools, teachers should not only impart corresponding music knowledge to students but also focus on cultivating students' comprehensive quality and quality. In the process of music teaching in universities, we should focus on students' psychological adjustment and emotional health care. In order to quantify the application effect of big data technology in music teaching reform, this paper takes piano teaching in universities as an example to conduct research and mainly analyzes the physiological and psychological prediction effects involved in the teaching process. This paper introduces three typical big data technologies to predict and analyze the data involved in the existing piano teaching in universities. The analysis results show that the prediction effect based on the fuzzy neural inference system is the best. In addition, the three-dimensional display of the prediction data shows that the prediction effect obtained by big data technology shows good consistency and continuity, which indicates that the prediction method based on big data technology is suitable for psychological adjustment and emotional health care in music teaching in universities. [ABSTRACT FROM AUTHOR]

In order to create an information construction road suitable for its own development faster and better, this paper has established a smart campus through new technologies such as big data. By increasing the application of data, cloud computing, and internet of things technologies, colleges and universities have improved their ability to comprehensively collect and efficiently process information. Combined with big data, cloud computing, and internet of things technology, this paper analyzes the construction of smart campus in colleges and universities. Finally, it summarizes the integration of big data, cloud computing, and internet of things technologies in the construction of smart campus in colleges and universities. The construction of intelligent campus card in colleges and universities can not only give full play to the advantages of campus network, but also use a card instead of various certificates of teachers and students in various consumption places on campus. In the future, with the sustainable development of information technology, smart campus will occupy a higher proportion in school management. The university must constantly improve the application level of big data and cloud computing technology in the smart campus. Therefore, in the new era, major universities should deeply understand the importance of smart campus construction and improve the application efficiency of advanced technology. [ABSTRACT FROM AUTHOR]

Actively responding to population aging has become the new national condition of China's aging society in the new era. To do a good job of actively coping with aging in the new era, it is necessary to take multiple measures and work together on the basis of clarifying the major realities and problems faced by China's aging society. From the perspective of big data technology, this paper systematically studies the concerns of China's aging through the method of artificial intelligence. In addition, with two typical aging indicators, three coupled intelligent algorithms are introduced in this paper. The prediction results show that the multi-dimensional support vector machine optimized by the immune algorithm shows good advantages compared with the other two algorithms. The multi-dimensional support vector machine optimized by the immune algorithm, one of the big data, has a good prediction effect on the performance indicators of China's aging, which indirectly illustrates the applicability of big data analysis to the concerns of China's aging. [ABSTRACT FROM AUTHOR]

Society is in urgent need of English talents with high comprehensive English application ability and strong independent learning ability. Task-based language teaching has always been an effective way to improve English talents, but the shortcomings of traditional task-based language teaching in teaching practice are beginning to emerge, such as insufficient classroom time, lack of real-time monitoring, and scientific evaluation mechanisms. Therefore, the traditional task-based language teaching cannot meet the new requirements of society for the cultivation of English talents. Random matrix theory uses the principles of statistical mechanics to model the interaction of complex systems in multiple mathematical domains to improve the teaching effectiveness. Random matrix theory has been widely used in theoretical neuroscience and optimal control. This paper is based on teaching task-based language and analyzing the data from the teaching process with big data so as to give timely feedback and suggestions to teachers. This paper constructs a model of college English teaching based on random matrix theory using a task-based teaching model. Since the teaching model of college English is a typical high-dimensional nonlinear function problem, a network model of deep confidence network is used to determine the implicit relationship between input and output. To verify the effectiveness of the model, this paper conducts a practical study of the constructed model. The study shows that the task-based teaching model of college English based on random matrix theory improves college students' English expertise and English language usage ability and also greatly enhances students' enthusiasm for learning English. [ABSTRACT FROM AUTHOR]

The combination and application of big data and machine learning in the offline consumer market makes the formulation of marketing strategies more scientific. The use of machine learning can make statistics and analysis of users' consumption behavior and classify customers according to users' consumption behavior, so as to realize the personalized promotion of marketing content. In this context, this paper carries out research on enterprise digital precision marketing strategy based on big data. Starting from the actual application, this paper analyzes the current problems faced by consumption data sharing, as well as the characteristic needs of precision marketing for consumer groups, introduces homomorphic encryption technology, completes the structural design, process design, and algorithm design of the scheme in combination with the actual scenario that enterprises need to carry out precision marketing for customers based on consumption data, describes each design link in detail, and verifies the feasibility of the algorithm in the scheme. [ABSTRACT FROM AUTHOR]

In this paper, the random matrix big data analysis model is thoroughly studied and constructed, the ecological model of college students' innovation and entrepreneurship education is analyzed, and the optimization model of college students' Innovation and entrepreneurship education environmental model based on the random matrix big data analysis model is designed. This paper briefly explains the random matrix and its M-P rate theory deduces the idea of feature extraction by the difference of eigenvalue limit spectrum distribution between different nonrandom matrices and random matrices, gives the data matrix representation method of FEMPL and the specific feature composition basis, and describes the steps of FEMPL feature extraction. A performance model for predicting the running time of Hadoop jobs is constructed using a random matrix. In this paper, innovation and entrepreneurship education has been carried out gradually, and the innovation and entrepreneurship education curriculum, platform, and mechanism have been progressively established. However, there is still a gap between the proper level of innovation and entrepreneurship education development. This study takes education ecology as the research perspective, analyzes the ecosystem of typical schools of innovation and entrepreneurship education, summarizes the dimensions and parameters of the invention and entrepreneurship education ecosystem, constructs an ecological model of innovation and entrepreneurship education for college students, and analyzes the problems and causes of the current innovation and entrepreneurship education ecology for college students based on the model, to propose specific strategies to promote the ecological development of innovation and entrepreneurship education for college students. [ABSTRACT FROM AUTHOR]

With the rising usage of technology, a tremendous volume of data is being produced in the current scenario. This data contains a lot of personal data and may be given to third parties throughout the data mining process. Individual privacy is extremely difficult for the data owner to protect. Privacy-Preservation in Data Mining (PPDM) offers a solution to this problem. Encryption or anonymization have been recommended to preserve privacy in existing research. But encryption has high computing costs, and anonymization may drastically decrease the utility of data. This paper proposed a privacy-preserving strategy based on dimensionality reduction and feature selection. The proposed strategy is based on dimensionality reduction and feature selection that is difficult to reverse. The objective of this paper is to propose a perturbation-based privacy-preserving technique. Here, random projection and principal component analysis are utilized to alter the data. The main reason for this is that the dimension reduction combined with feature selection would cause the records to be perturbed more efficiently. The hybrid approach picks relevant features, decreases data dimensionality, and reduces training time, resulting in improved classification performance as measured by accuracy, kappa statistics, mean absolute error and other metrics. The proposed technique outperforms all other approaches in terms of classification accuracy increasing from 63.13 percent to 68.34 percent, proving its effectiveness in detecting cardiovascular illness. Even in its reduced form, the approach proposed here ensures that the dataset's classification accuracy is improved. [ABSTRACT FROM AUTHOR]

For smart city traffic flow prediction in the period of big data and industry 4.0, the prediction accuracy is low, the prediction is difficult, and the prediction effect is different in different geographical locations. This paper proposes a smart city traffic communication forecast based on Industry 4.0 and big data analysis application. Firstly, this paper theoretically explains the application scenario of urban traffic fault text big data and analyzes the characteristics of related problems, especially the fault problems. Secondly, the AC traffic prediction algorithm is studied, and the application analysis of PVHH, IDT, and Ford–Fulkerson algorithms is applied, respectively. Finally, the above three algorithms are used to predict and analyze traffic flow. [ABSTRACT FROM AUTHOR]

In recent years, the tourism industry has grown rapidly around the world as an emerging force, especially in China, which has become the world's leading tourism country in recent years, and its tourism revenue also occupies a good weight in the country's total income. The number of tourists every year shows a very high growth rate, which not only improves the tourism economy but also brings great pressure to the management of various tourist attractions. In this context, many tourist attractions are actively developing innovative applications of new technologies, hoping to effectively improve their management efficiency. In these experiments, the speech big data analysis technology has achieved remarkable achievements, so it is necessary to combine the speech big data analysis technology with the demand analysis of the tourism industry. The purpose of this paper is to study the effective model of tourism consumer demand prediction using big data analysis. In the process of completing this paper, we consulted a large number of research results of big data analysis technology, tourism-related books, and demand prediction models in HowNet, VIP, and other network databases as well as campus libraries, summarized the related concepts of tourism, and used big data analysis technology to predict the demand of tourism consumers. Understand the needs of tourism consumers on major tourism websites, and extract the indicators that will affect the forecast results of consumer demand, establish a demand forecast model based on the indicators, and analyze its forecast effects through comparative analysis to understand its advantages and disadvantages, in order to establish a tourism demand forecast models providing actionable advice. Through the practical application case of the demand forecasting model, this paper puts forward the development strategy of tourism. The experimental results show that the mean square error of the neural network model is less than 2.5, which is more suitable for predicting the number of tourists, indicating that different models are suitable for predicting different indicators. The main contribution of this research lies in the modeling and analysis of regional tourism characteristics and tourists' willingness, so as to achieve accurate prediction of tourists in scenic spots and formulate targeted plans. [ABSTRACT FROM AUTHOR]

Using big data technology to promote the maturity and application of human analysis is the key to establish and maintain the competitive advantage of the school. Modern teachers must have the quality and ability to adapt to their work, that is, professional ability, in order to improve the teaching quality more pertinently. In order to effectively promote teachers' professional growth, this paper proposes a Chinese teachers' Ability Optimization Model Based on big data technology. Chinese teachers should take the initiative to meet the mission and challenges given by the times, strengthen Chinese teaching ability through the promotion of microability of information technology, explore the changes of learning and teaching style under the environment of information technology, and improve the ability of Chinese teachers. The research on the ability optimization of Chinese teachers based on big data technology in this paper can help schools optimize the training plan system by using the winning power model, that is, the training plan system can be improved from three aspects: determining the training objectives and contents and selecting the training methods. Teachers must improve their abilities through continuous learning, so as to promote education more actively and cultivate more talents for the country. [ABSTRACT FROM AUTHOR]

Scientific computing visualization is also known as visualization. In an academic sense, visualization is a way of computing. It can convert data into intuitive geometric images, making it easier to understand. Visualization is the use of computer graphics and image processing techniques. It is a technical means to convert piles of seemingly disorganized data into graphics or images through special computer methods. Recording is the process of recording sound signals on a medium. Specifically, it is the process of converting sound into electrical signals through microphones and amplifiers and recording them with different materials and techniques. Piano tuning is the adjustment of the strings so that they reach a certain high pitch. This paper aims to study the analysis and evaluation of multi-tone piano transcriptions based on scientific computing visualization and knowledge recognition algorithms. It expects to improve the quality of piano recording with the support of scientific visualization technology and knowledge recognition algorithm. This paper briefly explains the big data, and on this basis, the visual data mining technology is mentioned, and the current situation in the visual data mining is explained. A matrix calculation of the characteristic matrix of piano timbres was carried out to achieve the resynthesis of the 25th harmony in piano timbres electronically. The results of the trials in this article demonstrate that when the Gaussian number changes to 5 during the recording process, the difference that exists of the acoustic voltage intensity and the noise level phase is clearly diminished. When the Gauss figure is 10, there is an inversion of the value of the sound pressure phase. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet, new media news has gradually become the most concerned information source of new media people. New media public opinion is a force that cannot be ignored. It needs monitoring and guidance. The research on hot topic discovery and trend analysis can timely find social hot topics and analyze the trend of topics, which is conducive to grasp the trend of public opinion, so as to correctly guide and maintain social stability. At the same time, the emerging industry of new media has sprung up like mushrooms after a rain, with a rapid momentum. With the advent of the era of big data, the development of new media presents characteristics and advantages that are different from traditional media, but we should also note that the era of big data has both advantages and disadvantages for the development of new media. In this paper, LDA and ARIMA models can be used to calculate and analyze the popularity measurement and trend analysis of new media reports under the background of big data mining. The model designed in this paper has a conclusion: the missed detection rate is reduced by 75.4%. Through experiments, it is found that the accuracy of heat topic detection of the model designed in this paper can reach 84.6%. In trend analysis, the first stage of transmission is called the incubation period. Then after a certain critical point, it will come to the outbreak period. The outbreak period lasted for a period of time, entered a period of plateau, and finally came to a subsidence period. [ABSTRACT FROM AUTHOR]

With the continuous growth of the film market and the consumption demand of audiences, the value of film content has become increasingly apparent. Extracting movie content elements is an important step in quantitative analysis of movie content. In this paper, based on text mining technology, deep data analysis of movie reviews is carried out using TF-IDF and machine learning to visualize high-frequency words, and sentiment analysis is performed on reviews to find out the hidden deep information behind the big data of movie reviews. Although the extracted keywords can draw the content and characteristics of the film for us, they cannot establish the correlation with the creative elements of the film and television works. Therefore, in this paper, the extracted keywords are clustered to find the central words of these words. First of all, in order to improve the representativeness of keywords, the Epoch data set and the Batchsize data set are used to conduct experimental analysis on the model in this paper. Through comparative experiments, it is concluded that when Batchsize = 32 and Epoch = 25, the model achieves the optimal classification effect. The analysis shows that when the training times are too small, the model is not fully learned, and when the training times are too many, the model will be overfitted, which will reduce the accuracy of the model, resulting in the opposite effect. The keywords extracted above can draw the content and characteristics of the movie for us. [ABSTRACT FROM AUTHOR]

In recent years, the development of intelligent iteration technology and the use of big data processing technology have set off an upsurge, and the analysis and application of artificial intelligence algorithms have been paid more and more attention. In order to face the challenges of Arab migration and international trade, this paper constructs the basic structure of the Arab migration action imitation model. In this paper, a simulated servo clustering algorithm based on big data and intelligent iteration is used. Then, through the analysis of pseudo servo clustering algorithm, an optimization model is established, and a big data analysis system is formed. This paper focuses on the wide application of big data statistics to solve the construction of Arab immigration and entrepreneurship data system. This paper studies and applies the big data statistics and intelligent iterative algorithm of Arab immigration behavior, focuses on the annual ladder degree of Arab immigrants, and constructs a pseudo servo cluster system based on Intelligent iterative algorithm. Finally, the simulation experiment verifies whether the clustering model can accurately retrieve the behavior of Arab immigrants in China. The era of big data provides good development opportunities for Arab immigrants and international trade, but it also faces severe challenges. The study provides a reference for strengthening the analysis of international trade, and puts forward perfect countermeasures in combination with the actual situation, so as to improve the efficiency of international trade management and promote the better implementation of international trade. [ABSTRACT FROM AUTHOR]

With the advancement of science and technology as well as the continual improvement of big data analysis technology, the accuracy of traditional data information classification has declined, making it impossible to assess English ability effectively. A competency evaluation model for college English teaching vacancies is built using this information and the big data architecture. The ability of the big data information model is evaluated and feature information of ability constraints is extracted using the predefined constraint parameter index analysis model. Simultaneously, the K-means clustering algorithm is used to cluster and integrate a series of index parameters of English ability using big data, and the English teaching resource allocation plan is completed in accordance with this, allowing for the scientific evaluation of English teaching ability. The results of the studies show that the clustering method utilized in the context of big data can aid in the evaluation of English competence. In the experiment, four test cycles of English teaching skills were set up, and the effectiveness of the English evaluation techniques described in this paper and two classical cluster evaluation methods were compared and tested. The research shows that using the method described in this paper to evaluate English teaching skills can significantly improve the full utilization of data. [ABSTRACT FROM AUTHOR]

Rural cultural and creative industries continue to flourish. Because big data technology is fast and convenient, promoting the development of rural cultural and creative industries through the big data management system has attracted more and more attention. This paper constructs a big data management system platform and applies it to rural cultural and creative industries between different cities. Then, the paper systematically analyzes the development of rural creative industries and points out the corresponding measures to develop the rural creative industries. In general, this paper provides some research methods and experimental support for the cultural and creative industries. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet, the information on the Internet presents an explosive growth. Cloud computing and big data analysis technology based on Internet information rise accordingly. However, all web pages contain not only important information but also the noise information irrelevant to the subject information. They seriously affect the accuracy of information extraction, so the research of web page information extraction technology arises at the historic moment and becomes the research hotspot. The quality of web page text information will directly affect the accuracy of later information processing and decision-making. If we can accurately evaluate the information of the web pages captured from the Internet and classify the extracted web pages according to the corresponding characteristics, we can not only improve the efficiency of information processing, but also improve the practical value of the information decision-making system. From the practical application requirements and user-friendly operation point of view, the information visualization of web design based on big data is studied in this paper. Specifically, the system designed in this paper improves the traditional template-based web information extraction method, establishes a web information extraction rule scheme combined with templates, and achieves the goal of web information extraction rule selection and template generation in the visual environment. Finally, the visualization algorithm based on T-SNE verifies the effectiveness of the web page information visualization algorithm designed in this paper. [ABSTRACT FROM AUTHOR]

The rapid development of digital economy puts forward theoretical and practical requirements for the construction of legal model of big data transaction management (BDTM). The new problems of ambiguous data ownership, privacy leakage, chaotic digital transaction order, and low nationalization in the context of big data transactions have largely restricted the role of China's big data transaction market and related laws and regulations and are not conducive to promoting the scientific and standardized development of China's digital economy. In this paper, we firstly analyze the connotation, importance, and necessity of the construction of legal model of big data transaction in the era of digital economy from the basic connotation of legal model. Then, we analyze the need to adhere to the principles of systematization, security, and openness in the construction of legal model of big data transaction management and focus on four methods of contract law, market behavior, replication behavior, and dispute resolution in the construction of legal model of big data transaction management. Finally, we summarized the conclusions of this paper. The conclusions of this paper are important for promoting the transformation of digital resources into effective data elements and advancing the scientific and standardized development of China's digital economy. [ABSTRACT FROM AUTHOR]

In recent years, China has been paying more and more attention to the development of the sports industry, and many sports are no longer seen as a mere sport, but can be developed into an industry and play an important role in the development of the economy. This paper examines the application of multi-source big data mining techniques in the analysis of basketball economic management. Firstly, through multi-source big data mining technology, we collect various factors that influence the development of basketball economic industrialization, use Hash Tree-based Apriori algorithm to mine various influencing factors for basketball economic industrialization, and analyze the correlation between each influencing factor. The association rule mining results are then used to analyze the relationship between the key influencing factors and the industrialization of the basketball economy. This paper examines various aspects of the Chinese basketball league market, including the management system, market operations, and talent flow, and compares them with the foreign basketball industry models, in order to analyze the operation of China's basketball industrialization and develop corresponding countermeasures to improve basketball economic management based on the results of the study. [ABSTRACT FROM AUTHOR]

With the booming of big data in finance, data mining technologies, as a new method of data statistics, have made superior economic applications available to researchers. Based on the relationship between shadow banking and interest rate liberalization, this paper intended to analyze the bidirectional relationship between shadow banking and interest rate liberalization using big data mining technologies. The relationship between data mining and the economic application of shadow banking was proven using data from 2014 to 2021. On this basis, this paper identified the bidirectional influence between shadow banking and interest rate liberalization. The findings show that shadow banking has positive contributions to the liberalization of interest rate, and the interest rate distortion resulting from interest rate control also drives the development of shadow banking. Moreover, feasible suggestions have been proposed for supervision on policies. [ABSTRACT FROM AUTHOR]

Although conventional business models have been increasingly affected in front of the big data technology application, it has also brought new opportunities and challenges for enterprise development. In order to create a higher value, enterprises should keep pace with the times and actively develop business innovation service models. The greatest value brought by data is to help enterprises find potential business value. It can provide a broader user market and channels, avoid homogeneous competition, and realize the integration of upstream and downstream value chains. In addition, it abandons the extensive development under the traditional model and allows enterprises to return to real value services, which is also an irresistible trend of business model transformation. This paper studies and analyzes business innovation service models. First, the business model as required is presented, and the management system and risk evaluation method are introduced. Then, the construction of the business service model is discussed, and the typical big data technologies are reviewed. Next, according to the evaluation theory of business model, the index system of business innovation service model is explored, which can examine the development of business model objectively and comprehensively. Last, the operations of the business model under the big data are analyzed. The research on the business model in this paper can be provided with universality and has a certain practical value for the development of business innovation service. [ABSTRACT FROM AUTHOR]

Now, there are some problems in the art teaching system of colleges and universities, such as low management efficiency. However, with the development of big data technology, big data has been applied to the design and management of art teaching systems in colleges. Firstly, a model of art teaching system through the relevant big data was established; then the process of establishing the model was also discussed. Subsequently, based on the interactive design method and Hyperlink-Induced Topic Search (HITS) algorithm as the web design mode, this paper establishes the teaching system management platform and applies it to the art teaching in colleges and universities. The teaching evaluations of students and teachers of different grades were analyzed, and the application prospect of the teaching system design and management platform was obtained. In general, this paper indicates a theoretical reference for optimizing the effect of art teaching and realizing the design and management method of art teaching system under the background of big data. [ABSTRACT FROM AUTHOR]

E-payment has penetrated every aspect of people's daily life, and the development and application of E-payment technology have made life more convenient. Besides, the process of E-payment has endless hidden dangers, posing a great threat to payment security. In this context, risk assessment and prediction of E-payment are particularly important. Therefore, this paper mainly studies the E-payment analysis and prediction method based on big data technology. Specifically, this paper uses the BP neural network to extract E-payment data feature and compare and analyze the characteristic of different methods; finally, the payment risks are predicted based on the features, the simulation experiments show that the best result is obtained by the method proposed in this paper, and thus the effectiveness of the new method is verified. [ABSTRACT FROM AUTHOR]

At present, in order to adapt to the development of the era of big data, more and more industries use the concept of big data, and the economic and financial management system is no exception. In the context of the concept of big data, the campus economy has also ushered in new opportunities and challenges. Combined with the basic knowledge of big data, this paper makes a systematic research on industrial economic information analysis, which will contribute to the application practice of industrial big data. Using the value of big data will promote revolutionary changes in the work of the school's economic management department. Taking this as the starting point, this paper systematically analyzes the analysis methods of campus economic information under the background of big data and its role in macro decision-making. [ABSTRACT FROM AUTHOR]

Regarding the correction of X-ray beam hardening in the current CT imaging system, the traditional method will cause the overlapping of images during use, which will gradually harden the beam, and the image reconstructed by the imaging system will gradually become "cup-shaped" or "striped." "False images" seriously degrade the quality of the images, while causing more interfering diagnostic problems. In this paper, the cloud computing big data analysis algorithm is applied to the X-ray beam hard correction process. According to the transition energy and its energy absorption, the X-ray beam is used as the cut-in point, and the relationship between the attenuation coefficient of similar materials and the X-ray energy is used to remove the artifact image in the initial image reconstruction process to obtain a clear corrected image. Meanwhile, according to the thickness and gray value of the X-ray penetrable object, the result of fitting using a polynomial function is calculated, and an accurate line fitting can be completed for data with smaller coordinates. Finally, the experimental study shows that the cloud computing big data analysis proposed in this paper can detect X-ray beams in real time. This method uses optical receivers to achieve high noise sensitivity to X-rays, and in long-distance transmission scenarios, the bandwidth of communication transmission can be maximized, and different types of formats can be used to complete modulation for different X-rays. Therefore, X-ray beam hardening correction technology has better advantages and the market application scenarios compared with other technologies. [ABSTRACT FROM AUTHOR]

Human resource management mode refers to a comprehensive summary of management objectives, processes, content, methods, and other elements. The more common two modes are control mode and commitment mode. The enterprise human resource management model has many different types. The generation pair promotes the development of enterprise human resource management from the traditional model to the platform model, processing complex data with the help of data-based technical means, and realizing the integration and sharing of resource data. This paper takes an energy company as an example to carry out a detailed study. The article takes the big data as the background and the company as the research object. From the perspective of human resource management, this paper tries to find out the performance management, compensation and benefits management, and other issues of the company in human resource management under the background of the big data era and puts forward corresponding solutions for the current problems. In particular, the company gave certain opinions on how to build a human resource management system in the context of the current big data era. By conducting field research on the company and issuing questionnaires, this paper finds out the current problems of the company in human resource management and proposes corresponding solutions for these problems. [ABSTRACT FROM AUTHOR]

Due to the continuous progress of the era of mega data, it is more and more important to carry out accurate marketing of online video. Based on the analysis of users' big data, this paper proposes and implements an online video precision marketing algorithm, which provides a reference for the precision marketing strategy of video websites. This paper introduces the concept and characteristics of precision marketing and analyzes the application forms of precision marketing of video websites. It also summarizes the operation system, operation mode of precision marketing, and the influence of online video advertising on enterprises and users. In order to realize the accurate marketing of big data, it needs to be carried out from three parts: data collection, data analysis, and marketing plan formulation and implementation. Through continuously collecting customer data, it gradually and clearly acquires the characteristics of customers and constantly dynamically adjusts the marketing strategy to customers, so as to accurately conduct video marketing to customers and greatly improve the marketing efficiency. In order to test the effectiveness of the marketing algorithm in this paper, we conducted an experiment. The research in this paper has important practical value and reference significance for reducing the cost of video advertising of enterprises and improving the communication effect of online video advertising. [ABSTRACT FROM AUTHOR]

With the development of the national economy, the interior decoration work has received extensive attention, and the traditional decoration methods have been unable to meet its actual work needs. The application of mirror materials in interior decoration can not only enrich the technical methods of interior decoration, but also improve the decoration quality and comfort, so as to meet people's growing decoration needs, achieving the purpose of promoting the sound development of interior decoration. Mirror material is one of the decorative materials widely used in modern indoor environment. It not only has decoration, but also can expand space. In recent years, the advent of the era of big data has promoted the reform of the design industry. Using big data thinking to assist the innovative design of furniture products will also become an important trend in the development of the furniture industry. According to the characteristics of customer demand in the era of big data, this paper designs and develops a set of customer demand analysis system based on big data by using cloud computing and big data mining technology. By collecting the behavior data of users when using products, this paper combines the mirror material with the manufacturing process of wooden furniture. The mining algorithm is applied to find out the improvement goal and direction of wood furniture manufacturing process based on mirror material, so as to enhance the competitiveness of furniture enterprises. The method proposed in this paper can obtain the furniture design elements according to the functional zoning and theme information and input the furniture design elements and zoning elements into the design element matching evaluation model to obtain the design matching element information. [ABSTRACT FROM AUTHOR]

The roof water hazard has severely weakened the safety of coal mine production. As the most direct data on the state of the roof, the monitoring big data of the hydraulic support is of great importance to coal mine safety. This paper proposes a neural network model based on multitimescale feature extraction for predicting the pressure-bearing data of hydraulic supports in coal mine working faces, drawing on the idea of multiscale time feature extraction in the empirical modal decomposition algorithm. The proposed model can excel in capturing temporal features at multiple scales in the time series, thus improving the prediction accuracy of the neural network model. We collected 80 days of monitoring data from 17 hydraulic supports in the working face and carried out comparative experiments with several existing models. The experiments show that the neural network model proposed in this paper is stable and reliable, with small prediction errors, and can provide important help for the prevention and control of the roof water hazard. [ABSTRACT FROM AUTHOR]

The rapid development of digital economy puts forward theoretical and practical requirements for the construction of legal model of big data transaction management (BDTM). The new problems of ambiguous data ownership, privacy leakage, chaotic digital transaction order, and low nationalization in the context of big data transactions have largely restricted the role of China's big data transaction market and related laws and regulations and are not conducive to promoting the scientific and standardized development of China's digital economy. In this paper, we firstly analyze the connotation, importance, and necessity of the construction of legal model of big data transaction in the era of digital economy from the basic connotation of legal model. Then, we analyze the need to adhere to the principles of systematization, security, and openness in the construction of legal model of big data transaction management and focus on four methods of contract law, market behavior, replication behavior, and dispute resolution in the construction of legal model of big data transaction management. Finally, we summarized the conclusions of this paper. The conclusions of this paper are important for promoting the transformation of digital resources into effective data elements and advancing the scientific and standardized development of China's digital economy. [ABSTRACT FROM AUTHOR]

With the continuous acceleration of the urbanization process, rural areas have gradually put forward construction plans for new rural forms. Pastoral complex is a construction mode of characteristic townships and rural complexes, which has become a new form of social development today. Pastoral complexes provide a new model of new rural construction, and its development is still in the preliminary stage for China. The construction of pastoral complexes involves rural landscape planning, economic management, etc. It is difficult for rural managers to process these complex data characteristics. Big data technology is affecting people's lives, and the discipline of landscape architecture is also deeply affected by big data. This paper mainly uses data mining technology and neural network method to carry out feature mining and prediction on the landscape planning and management methods of beautiful rural pastoral complexes. In this paper, a data mining algorithm is used to classify the landscape types and locations of rural complex, and the neural network method is used to predict the landscape characteristics. The results show that the methods of data mining and neural network are feasible and reliable both in the proportion of classification and in the prediction of landscape planning. The maximum error of the prediction of landscape design is 2.87%, and the minimum error is only 0.79% using the neural network method, which is an acceptable error range. [ABSTRACT FROM AUTHOR]

In the era of the rise of big data, if insurance companies can effectively and reasonably use existing data to tap more potential information, it can not only improve work efficiency but also achieve precise marketing to customers, thereby saving costs. At the same time, China pays more and more attention to financial insurance, so it is of great significance to use customer information to explore the purchase behavior of financial insurance. This paper mainly analyzes the factors that affect the purchase of financial insurance from the aspects of individuals and families and provides references for insurance marketing. This paper selects the results of the China Comprehensive Social Survey in 2020 as the empirical sample data of this paper and sets whether to purchase financial insurance as the target variable. The characteristic variables were screened, and the 10 variables selected after screening were finally used to build the model. Next, the data are divided into training set and test set, and a decision tree learning model is established on the two data sets at the same time. The classification results of the model are evaluated according to the classification evaluation indicators. Finally, the decision tree analysis results provide suggestions and strategies for the construction of insurance marketing recommendation methods. [ABSTRACT FROM AUTHOR]

By analyzing the historical background of the times and summarizing the current situation of urban development research at home and abroad, we find that there is a lack of theoretical and practical research related to intelligent park cities. Therefore, this paper first starts with the connotation of park city theory, focuses on the improvement of planning methods, and studies the application paths of big data technology in the planning and construction of park cities. Taking Jinzhai Country as the research object, this paper further illustrates the applicability of this research method at the macrolevel. In the context of the park city, we analyze the problems of the current status of city construction in general and explore the path of "planning concept first, big data-assisted design" for innovative and intelligent city construction. According to the study, the overlook corridor control has an impact on the building height. In terms of landscape protection, overlook system simulation is the other key factor. In addition, the analytic hierarchy process is the basis for development intensity control. The results show that big data technology can assist in the landscape conservation, morphology formation, and efficient operation of Jinzhai Country Park City. Our aim is to achieve the protection and utilization of its ecological environment and natural resources and thus to comprehensively coordinate the multidimensional urban spaces and build a park city model. As an urban development model that meets the requirements of being "people-oriented, efficient, green, and aesthetic" in the new stage, park cities need to be faced by researchers in order to further realize the overall city goal and vision for the whole society to be smart. It also provides relevant design ideas and methods for the planning and construction of park cities in other similar cities or regions. [ABSTRACT FROM AUTHOR]

College students' career planning refers to the process of establishing their own career development goals based on the analysis of their own subjective and objective factors, searching for a career, and taking necessary actions to achieve their career goals. Career planning is an important aspect of human resource management, and it is a new trend in recent research to use the theory and method of psychology to study it. This project understands the current situation of contemporary college students' career planning through investigation and finds the common problems in career planning among college graduates. Systematic analysis of these problems can be used as the research premise to study college students' career planning, help college students to make correct self-assessment and career orientation, improve their self-awareness and evaluation ability, and form a good career self. In the research, the author found that, in the guidance of college students' career development direction, it is necessary to predict the future career development direction of college students. Effective prediction methods can provide objective and simple data support and theoretical basis for educators to help them implement education guidance smoothly. Therefore, this paper has carried out a series of research work. The author firstly analyzes the problems existing in the process of college career education, then analyzes the relevant theories and common methods of educational data mining and the dimension analysis of the influencing factors of college students' career development direction, so as to determine the research dimension of this paper, and then carries out the relevant research such as the modeling of prediction model, the implementation of algorithm, and the case test of prediction method. According to the above research, the author draws the following conclusions: the grey prediction algorithm has better performance in small- and medium-sized data prediction. Based on the comprehensive quality evaluation data, the prediction conclusion is more objective and accurate. However, there are still some shortcomings in the algorithm design in this study. In the future research, we can try to adjust the relevant parameters through more data and more experimental verification, so as to further optimize the algorithm. [ABSTRACT FROM AUTHOR]

In this study, coal and gas outbursts are the "biggest killer" of mine safety production. With the deepening of mining, the mine gas disaster is becoming more and more serious. From the perspective of big data, the establishment of a dynamic visualization system of gas geology integrating gas geology, gas drainage, dynamic outburst prevention, and other information can effectively improving the defects of gas disaster prevention and control in the coal industry, such as insufficient advance, lack of systematic identification methods and backward information collection methods, which is of great significance to improve the ability of gas hazard identification. Based on the precise detection of geology, structure, and gas, this paper proposes to use information technology, the Internet of things, big data analysis, and other technologies to comprehensively analyze the changes in gas occurrence and coal seam occurrence on the basis of the causes of mine gas geological outburst, fully consider the logical relationship between different factors and outburst and adopt the disciplinary advantages of grey theory, fault tree theory, BP neural network, and so on. The tree of coal and gas outburst accidents with general significance is constructed by 24 relatively independent factors. The input vector is determined as the matrix composed of eight main factors affecting and controlling outburst, including gas pressure, coal mechanical strength, comprehensive characteristic coefficient of coal fragmentation, the permeability coefficient of coal, comprehensive characteristic coefficient of coal seam bifurcation and combination, comprehensive characteristic coefficient of coal thickness and coal thickness change, fault complexity coefficients and interlayer sliding comprehensive characteristic coefficient. The geological data affecting coal and gas outbursts are analyzed and calculated scientifically so that the gas geological data can be updated in time, and the change of gas geological laws is presented dynamically, so as to guide the mine to predict the gas disaster more scientifically and reliably. [ABSTRACT FROM AUTHOR]

In this paper, the intelligent education cloud service platform is first constructed in view of the high cost consumption in the process of resource sharing scheduling in colleges and universities. Secondly, the hierarchical education resource sharing grid model is proposed. Specifically, according to the characteristics of the educational resource grid, the key factors affecting the performance of copy creation strategy are analysed, and a dynamic copy creation strategy is proposed. A multiresource equity distribution mechanism based on the concept of resource sharing equity is further proposed. The mechanism establishes a planning model according to the limited task resource demand and the amount of resources shared by the user at different times so that the global cumulative share vector of superior resources meets the dictionary order optimally. The simulation experiment shows that the grid sharing model proposed in this paper has better performance on the educational cloud service platform. The proposed resource allocation mechanism has achieved good results in ensuring the fair distribution of resources and ensuring high resource utilization when resource sharing users put forward multiple groups of time-changing resource demands. [ABSTRACT FROM AUTHOR]

Big data has been developed for nearly a decade, and the information data on the network is exploding. Facing the complex and massive data, it is difficult for people to get the demanded information quickly, and the recommendation algorithm with its characteristics becomes one of the important methods to solve the massive data overload problem at this stage. In particular, the rise of the e-commerce industry has promoted the development of recommendation algorithms. Traditional, single recommendation algorithms often have problems such as cold start, data sparsity, and long-tail items. The hybrid recommendation algorithms at this stage can effectively avoid some of the drawbacks caused by a single algorithm. To address the current problems, this paper makes up for the shortcomings of a single collaborative model by proposing a hybrid recommendation algorithm based on deep learning IA-CN. The algorithm first uses an integrated strategy to fuse user-based and item-based collaborative filtering algorithms to generalize and classify the output results. Then deeper and more abstract nonlinear interactions between users and items are captured by improved deep learning techniques. Finally, we designed experiments to validate the algorithm. The experiments are compared with the benchmark algorithm on (Amazon item rating dataset), and the results show that the IA-CN algorithm proposed in this paper has better performance in rating prediction on the test dataset. [ABSTRACT FROM AUTHOR]

This paper uses big data technology to predict the employment rate of colleges and universities. In this paper, combined with the current rental price, daily life consumption, and college students' personal interests and hobbies consumption and other indicators, the individual is simulated by big data, and the individual is associated by using the AI-driven edge fog computing service optimization algorithm to form a cluster, so as to realize the prediction from element to neural network cluster by using edge computing. In addition, this paper takes the employment data of colleges and universities in Hunan province from June 2020 to May 2021 as the research sample to test the prediction model and makes a comparative analysis with the CNN model and LSTM model. The edge fog computing model in this paper has more analytical indexes as tuples compared to the CNN model, so the results show that the prediction accuracy can reach 83.25%. In this case, there is little difference between the two models of data processing and predictive efficiency. Compared with the LSTM based classification prediction model, this model is edge computing, which greatly improves the data quality of model and data parameters, and the calculation efficiency can be increased by 45%–65%. Therefore, the use of big data technology can provide a reference for the research direction of higher education. [ABSTRACT FROM AUTHOR]

Along with the development and promotion of Internet technology, new media are increasingly diversified, enriching and changing our lives. This paper focuses on outdoor interactive advertising as a communication method, analyzing its creative features and applications. Unlike traditional advertising, where audiences receive passively, new media interactive advertising establishes a more direct communication method for consumers and products, creating different sensory stimulation and emotional experience environments according to the characteristics of products and conducting a series of interactions with consumers' sensory systems such as vision, hearing, smell, taste, and touch. The advertising method has also changed from passive reception to active participation. Finally, combining the characteristics of accurate delivery and the diversity of presentation forms of interactive advertising under the big data platform, the theme conception and form design of interactive advertising is proposed. The interactive design principles of highlighting the simplicity of the theme are explored. The interactive design principle of highlighting the simplicity of the theme is proposed, and the combination of color, text, and graphics is explored by the aesthetic visual design of the audience and the premise of focusing on the audience's feelings. Starting from the visual effect of outdoor interactive advertising, the change in the way advertising is conveyed is interpreting to us the arrival of a new communication concept, the arrival of the Internet-centered and technology-centered data integration era. The research in this paper aims to provide useful support for the diversified development of outdoor interactive advertising in the new media environment by exploring how to skillfully realize the creative expression of outdoor interactive advertising in the new media environment. [ABSTRACT FROM AUTHOR]

In recent years, people have paid more and more attention to cloud data. However, because users do not have absolute control over the data stored on the cloud server, it is necessary for the cloud storage server to provide evidence that the data are completely saved to maintain their control over the data. Give users all management rights, users can independently install operating systems and applications and can choose self-service platforms and various remote management tools to manage and control the host according to personal habits. This paper mainly introduces the cloud data integrity verification algorithm of sustainable computing accounting informatization and studies the advantages and disadvantages of the existing data integrity proof mechanism and the new requirements under the cloud storage environment. In this paper, an LBT-based big data integrity proof mechanism is proposed, which introduces a multibranch path tree as the data structure used in the data integrity proof mechanism and proposes a multibranch path structure with rank and data integrity detection algorithm. In this paper, the proposed data integrity verification algorithm and two other integrity verification algorithms are used for simulation experiments. The experimental results show that the proposed scheme is about 10% better than scheme 1 and about 5% better than scheme 2 in computing time of 500 data blocks; in the change of operation data block time, the execution time of scheme 1 and scheme 2 increases with the increase of data blocks. The execution time of the proposed scheme remains unchanged, and the computational cost of the proposed scheme is also better than that of scheme 1 and scheme 2. The scheme in this paper not only can verify the integrity of cloud storage data but also has certain verification advantages, which has a certain significance in the application of big data integrity verification. [ABSTRACT FROM AUTHOR]

This article first addresses the problem that the unstructured data in the existing e-learning education data is difficult to effectively use and the problem that the coarser granularity of sentiment analysis results in traditional sentiment analysis methods and proposes multipolarized sentiment based on fine-grained sentiment analysis evaluation model. Then, an algorithm for behavior prediction and course recommendation based on emotional change trends is proposed, and the established multiple linear regression equation is solved with an improved algorithm. Finally, the method in this paper is verified by a comprehensive example with algorithm comparison analysis and cross-validation evaluation method. The research method proposed in this article provides new research ideas for evaluating and predicting the learning behavior of e-learners, which is conducive to timely discovering learners' dropout tendency and recommending relevant courses of interest to improve their graduation rate, so as to optimize the learning experience of learners, promote the development of personalized education and effective teaching of the e-learning teaching platform, and provide a certain reference value for accelerating the reform process of education informatization. In order to improve the speed of searching for parameters and the best parameters, this paper proposes a particle swarm algorithm (to improve the support vector machine parameters in a sense) and finds the best parameters which also achieved the goal from academic expression to academic performance. [ABSTRACT FROM AUTHOR]

With the analysis of mobile clients and artificial intelligence technology, e-commerce is growing faster and faster. In today's daily life, the e-commerce model has even been integrated into human life and has become an indispensable part. In the past few years, in the face of such a large business opportunity, e-commerce companies have sprung up in the capital market. Along with the rise of e-commerce, there have been many problems that have never arisen. The purpose of this paper is to analyze the status of development of e-commerce and its enterprise. This paper focuses on combing the current marketing overview and problems of e-commerce companies and proposes positive countermeasures and some practical solutions. Using the specific analysis method of the specific problem, the data comparison is made to draw conclusions. The results show that national policies focus on the rise of small and microenterprises, from the perspective of current business development, and developing e-commerce is conducive to enterprises to expand business breadth, expand the market scope of enterprises, develop business needs and brand products of enterprise by using Internet technology, and can break the constraints of time and space effectively. Strengthened communication and communication between enterprises and customers reduced the costs of enterprise, saved time, and improved utilization rate of resource. Therefore, solving the marketing problems which is most urgent at present is the primary goal of developing e-commerce companies. [ABSTRACT FROM AUTHOR]

The era of big data and cloud computing has come, communication between different languages is becoming more and more common, and the barriers between languages are becoming more and more prominent. As the most important means to overcome language barriers, machine translation will play an increasingly important role in modern society. The previous machine translation technology has more or less disadvantages. The accuracy of translation is too low, which is a huge bottleneck hindering the further development of machine translation technology. Therefore, based on this, we can consider modeling the cross-context accurate English translation model based on the machine translation model and rely on the working principle of machine learning. This experiment shows that the translation accuracy of our method reaches 94.2%, which is higher than 39.5% of the benchmark method. This shows that the method in this paper can reduce the influence of other factors, ensure the accuracy of cross-context English translation to a certain extent, and meet the performance improvement requirements of the English translation system. [ABSTRACT FROM AUTHOR]

This paper carried out an analysis and evaluation research of engineering job demand based on big data technology. By collecting the semistructured or unstructured recruitment information of engineering positions on the recruitment website, text mining technology is used to mine the knowledge model hidden in the market by building a relatively perfect Dictionary of professional skills of engineering positions. Based on the large sample data, a comprehensive, multidimensional and high-precision postdemand characteristic model is constructed. The model can not only interpret the existing recruitment market and elaborate the specific skill needs of different positions but also predict and analyze the model and estimate the required skills in combination with the specific postresponsibility characteristics, which can not only enable candidates to submit resumes reasonably, Moreover, it provides suggestions for colleges and universities or other relevant employment institutions to have an accurate, comprehensive and in-depth insight into market demand and carry out effective talent training for all units. Through the analysis of the experimental results, it is proved that the established engineering postdemand analysis model is consistent with the actual situation, and has certain explanatory significance for the current economic and social recruitment phenomenon, and the model has reference value. [ABSTRACT FROM AUTHOR]

In order to improve the automatic evaluation ability of fine arts education effect in colleges and universities, this paper proposes the evaluation research of fine arts education effect based on big data technology. We build university fine arts education effect data analysis model, considering the characteristics of the college art education effect; can reflect the effect of art education in colleges and universities' selected index system, with the effect of art education in colleges and universities of the decision about the elements of the decomposed into goals, standards, and plan level, such as university fine arts education effect evaluation of qualitative and quantitative analysis; find out the hidden representative university fine arts education effect evaluation factors; build fine arts education in colleges and universities' effect-associated distribution rules of the data, through unsupervised learning method, the effect of art education in colleges and universities' data feature extraction in the process of adaptive learning, by fuzzy comprehensive evaluation of big data; and realize the effect of art education in colleges and universities. The test results show that the fitness level of using this method to evaluate the effect of art education in colleges and universities is high, the score of the evaluation effect of art education in colleges and universities is significant, and it is in a good state in the evaluation index score table, indicating that the evaluation effect is accurate and reliable. [ABSTRACT FROM AUTHOR]